High-throughput Analysis of Capillary Density in Skeletal Muscle Cross Sections

Capillary density in skeletal muscles is key to estimate exercise capacity in healthy individuals, athletes, and those with muscle-related pathologies. Here, we present a step-by-step, high-throughput semi-automated method for quantifying capillary density from whole human skeletal muscle cross-sections, in areas of the muscle occupied by myofibers. We provide a detailed protocol for immunofluorescence staining, image acquisition, processing, and quantification. Image processing is performed in ImageJ, and data analysis is conducted in R. The provided protocol allows high-throughput quantification of capillary density. Key features • This protocol builds upon the method and results described in Abbassi-Daloii et al. (2023b). • It includes step-by-step details on image acquisition and image processing of the entire muscle section. • It enables high-throughput and semi-automated image quantification of capillary density. • It provides a robust analysis for determining capillary density over the entire muscle cross section.

This protocol is used in: eLife (2023), DOI: 10.7554/elife.80500Capillary density in skeletal muscles is key to estimate exercise capacity in healthy individuals, athletes, and those with muscle-related pathologies.Here, we present a step-by-step, high-throughput semi-automated method for quantifying capillary density from whole human skeletal muscle cross-sections, in areas of the muscle occupied by myofibers.We provide a detailed protocol for immunofluorescence staining, image acquisition, processing, and quantification.Image processing is performed in ImageJ, and data analysis is conducted in R. The provided protocol allows high-throughput quantification of capillary density.

Background
Capillaries in skeletal muscles play a vital role in the delivery of oxygen and nutrients essential for muscle metabolism and contraction, both at rest and during exercise.Capillary density, which refers to the number of capillaries in a given myofiber area, is critical for estimating oxygen consumption and determining exercise capacity in athletes, the elderly, and patients with muscle-related pathologies.Low capillary density in skeletal muscles is an indicator of reduced oxidative metabolism (Duscha et al., 2020).Conversely, a higher capillary density shortens the distance for oxygen diffusion, leading to improved muscle performance (Gliemann, 2016).Importantly, capillary density can adapt to different conditions and stimuli.For example, endurance training increases muscle capillary density, whereas physical or medical conditions associated with muscle disuse can negatively affect capillary density (Lemieux and Birot, 2021).Thus, determining capillary density is a key measure for assessing changes in skeletal muscle physiology and evaluating the exercise potential of skeletal muscles.Capillary density is defined as the number of capillaries per unit of muscle cross-sectional area in a muscle biopsy (McGuire and Secomb, 2003;Abbassi-Daloii et al., 2023b).This only takes into account the myofibers within the muscle tissue, and therefore excludes fibrotic regions.Determination of capillary density is essential for estimating oxygen consumption and blood flow in skeletal muscles.It involves immunohistochemistry in muscle cross sections using antibodies specific for proteins expressed in endothelial cells, such as CD31 and/or CD105 (endoglin) (Pestronk et al., 2010;Duscha et al., 2020).In some protocols, capillaries are stained with Ulex europaeus agglutinin 3 Published: Jan 20, 2024 (Hendrickse et al., 2022), which stains lectins (N-glycans) and is used as a marker for endothelial cells (Holthöfer et al., 1982).Most protocols for measuring capillary density rely on manual, eye-based evaluation of fluorescencestained muscle tissue, as shown in examples such as Andersen (1975), Duscha et al. (2020), andBaum et al. (2023).However, eye-based image scoring has limitations, due to its susceptibility to bias, time-consuming nature, and low throughput, resulting in reduced reproducibility and less robust results.Alternative procedures use image quantification, but these are also low throughput and cover only a small part of the muscle cross-section, leading to a spatial bias (Hendrickse et al., 2022).High-throughput semi-automated imaging and image quantification of the entire muscle cross section overcomes these limitations.We recently reported on a large study of human skeletal muscles that required high-throughput imaging and image analysis of immunohistochemistry in skeletal muscles (Abbassi-Daloii et al., 2023b).While we have previously presented a high-throughput protocol for myofiber typing (Abbassi-Daloii et al., 2023a), here, we present a high-throughput protocol for assessing capillary density in skeletal muscles.A flowchart summarizing the steps implemented in this protocol is shown in Figure 1.

Biological materials
1. Snap-frozen human skeletal muscle biopsy

A. Cryosection of skeletal muscle biopsies
The procedure is detailed in Abbassi-Daloii et al. (2023a).In brief, this step entails the preparation of muscle biopsies for histology and immunofluorescence staining.Following the cleaning of equipment and temperature adjustments, the muscle biopsies are equilibrated inside the cryostat.Subsequently, biopsies are embedded in Tissue-Tek, placed on specimen holders, and cryosections of a specified thickness (10-16 μm) are collected onto SuperFrost slides.Store slides at -20 °C or -80 °C prior to immunostaining.

B. Immunofluorescence
This step describes immunofluorescence staining using antibodies for CD31 and laminin.Adding anti-CD105 as a second marker for endothelial cells is optional.The antibodies are prepared in PBST. 1. Air dry slides from -20 °C for 30 min at room temperature (RT).2. Outline each section with an immunopen approximately 2-3 mm from the tissue edge.This reduces the required volume of the antibody mix.Note: Do not draw the line too close to the muscle sections as it can introduce artifacts in the image processing step.6 Published: Jan 20, 2024 3. Wash the sections in PBST. 4. Blocking: Cover each section with PBST + 5% milk (~40 μL) for 30 min at RT. 5. Wash the slides three consecutive times with a large volume of PBST (~40 μL), each time for 5 min.6.Primary antibody incubation: Cover each section with 20 μL of antibody mix containing anti-human CD31-Alexa Fluor ® 594-conjugated, rabbit anti-laminin, and anti-human CD105 biotin-conjugated (optional).Incubate for 2 h at RT. Note: Keep slides in the dark from this step onwards.7. Wash the slides three consecutive times with an excessive volume of PBST, each time for 5 min.8. Secondary antibody incubation: Incubate sections with 20 μL of mixture of the following secondary antibodies for 1 h at RT: goat anti-rabbit Alexa Fluor ® 750-conjugated to detect anti-laminin and Streptavidin-Alexa Fluor ® 647-conjugated to detect anti-CD105 (optional).9. Wash the slides three consecutive times with an excessive volume of PBST, each time for 5 min.10.Nuclei counterstain is carried out by a short incubation (5-10 min) of the section with a DAPI solution (1:1,000 dilution in PBST, ~20 μL per section) in a dark environment.Afterward, gently rinse the sections with PBST to remove excess DAPI solution.DAPI binds to nucleic acids and stains the chromatin.11.Mounting: Cover the sections with ProLong TM Gold antifade mountant (~10 μL per section).Cover the slide with a coverslip and fix it with nail polish.Note: Avoid any air bubbles on the sections as they will affect the image acquisition.12. Keep for 24 h at RT in the dark prior to imaging.13.Store slides at 4 °C prior to imaging.
Note: Slides can be kept at 4 °C for one month but imaging a week after immunostaining is preferable.

C. Image acquisition
The image acquisition is detailed in Abbassi-Daloii et al. (2023a).In brief, we utilized a Zeiss Axio Scan.Z1 slide scanner with the ZEN 2 software.Per fluorophore, exposure and intensity were determined to maximize signal-to-noise ratio without bleaching.Imaging was carried out with a 10×/0.45Plan-Apochromat objective.
For high-throughput imaging, we recommend image acquisition with a slide scanner with a stitching option.Note: It is crucial to optimize the imaging settings on a test slide to determine the appropriate exposure time and intensity for each fluorophore.Adjustments of exposure time and focusing algorithms may affect the visibility of the fluorophore signal.To achieve the best signal-to-noise ratio without causing bleaching, it is necessary to optimize the intensity and exposure time for each fluorophore/channel.
For all channels, utilize single band filters with the following excitation ranges: 7 Published: Jan 20, 2024 The procedure for converting image format and laminin segmentation is carried out in ImageJ/Fiji, using five sequential macros.The macros are found in: (https://github.com/tabbassidaloii/ImageProcessing/tree/main/CapillaryDensity/Macros/),macros 2-4.The macros are fully automatic, besides macro number 3, which might require a manual adjustment (as explained in the macro and in Abbassi-Daloii et al., 2023a).The outputs from the five macros are collected in a folder named "check" with mask images after each step, and a folder "ROI" with .txtfiles reporting the area of the segmented laminin objects that will be used for the calculation of capillary density.An example of laminin staining and segmentation is in Figure 2.

Segmentation and quantification of CD31 and CD105 objects
This step is executed using macro number 6.We perform the segmentation of the CD31 signal and compute the intersection with the laminin segmentation; these objects are considered as capillary.The output files contain the mean fluorescence intensity, area, and circularity of the capillary objects for both CD31 and CD105.a.A mask is made on CD31 images using a Gaussian Blur filter to reduce noise, resulting in a smoother image.b.Thresholding is applied using the "Li dark" algorithm to convert the CD31 channel into a binary image that distinguishes between foreground and background pixels.c.The Watershed algorithm is then utilized to accurately separate cells that may be touching or overlapping.d.The Fill Holes algorithm is employed to fill any empty spaces within the segmented regions.e.The laminin segmentation mask is added to identify the overlap between laminin and CD31 objects, resulting in a capillary mask.f.Once the capillaries have been segmented, the mean fluorescence intensity is measured in the original CD31 and CD105 channels.g.The intensity, circularity, and area of CD31 and CD105 are in the output file that is saved in a folder named "segmentation."An example of CD31 and CD105 staining, CD31 segmentation, and capillary mask are in Figure 2.

Figure 1 .
Figure 1.Flowchart of the main steps in the protocol.In the immunostaining step, an anti-laminin antibody marks the cell membrane, while anti-CD31 and anti-CD105 antibodies mark epithelial cells.

Figure 2 .
Figure 2. Visualization of capillary mask and capillary output generation.The images show the entire cross section with a zoom-in insert in the right bottom of each image.Red arrows point to laminin regions that were excluded from the mask after segmentation.Green arrows point to CD31 objects that did not overlap with laminin and were therefore excluded from the capillary mask.The capillary mask is used to obtain CD31 intensity and CD105 intensity.